arXiv subjects

Yimin Liu

Publications and source records attributed to Yimin Liu.

62 records · Page 4Linked to original sources

arXiv2016

The Random Frequency Diverse Array: A New Antenna Structure for Uncoupled Direction-Range Indication in Active Sensing

In this paper, we propose a new type of array antenna, termed the Random Frequency Diverse Array (RFDA), for an uncoupled indication of target direction and range with low system complexity. In RFDA, each array element has a narrow bandwidth and a randomly assigned carrier frequency. The beampattern of the array is shown to be stochastic but thumbtack-like, and its stochastic characteristics, such as the mean, variance, and asymptotic distribution are derived analytically. Based on these two features, we propose two kinds of algorithms for signal processing. One is matched filtering, due to the beampattern's good characteristics. The other is compressive sensing, because the new approach can be regarded as a sparse and random sampling of target information in the spatial-frequency domain. Fundamental limits, such as the Cramér-Rao bound and the observing matrix's mutual coherence, are provided as performance guarantees of the new array structure. The features and performances of RFDA are verified with numerical results.

cs.IT↗

arXiv2015

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems

We present a machine learning algorithm for building classifiers that are comprised of a small number of disjunctions of conjunctions (or's of and's). An example of a classifier of this form is as follows: If X satisfies (x1 = 'blue' AND x3 = 'middle') OR (x1 = 'blue' AND x2 = '<15') OR (x1 = 'yellow'), then we predict that Y=1, ELSE predict Y=0. An attribute-value pair is called a literal and a conjunction of literals is called a pattern. Models of this form have the advantage of being interpretable to human experts, since they produce a set of conditions that concisely describe a specific class. We present two probabilistic models for forming a pattern set, one with a Beta-Binomial prior, and the other with Poisson priors. In both cases, there are prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide two scalable MAP inference approaches: a pattern level search, which involves association rule mining, and a literal level search. We show stronger priors reduce computation. We apply the Bayesian Or's of And's (BOA) model to predict user behavior with respect to in-vehicle context-aware personalized recommender systems.

cs.LG↗

arXiv2014

High Range Resolution Profiling in Missing Data Case: A New Approach

We have proposed a novel method for Synthetic High Range Resolution (HRR) profiling, under the condition of missing frequency domain samples. This new approach estimates the autocovariance function (ACF) of the signal by valid sample pairs. Autocovariance matrix is formed from ACF estimations. Even with large part of data missing, new approach exhibits robust profiling result. Simulations are presented to show a advantage over other approaches in missing data case. Moreover, a real radar experiment was conducted to validate the new approach.

cs.IT↗

arXiv2014

Adaptive matching pursuit for off-grid compressed sensing

Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.

eess.SP↗

arXiv2013

Fundamental Limits of HRR Profiling and Velocity Compensation For Stepped-Frequency Waveforms

The stepped-frequency (SF) waveform is an effective way to achieve high range resolution (HRR) in modern radars. In this paper, we determine some fundamental limits of SF waveforms on ambiguity, stability and accuracy of stable targets profiling, and velocity compensation accuracy of moving targets. The investigation shows that via using the information contained in both phase and envelop of the echo signal, the radar can achieve HRR profiles without ambiguity under a looser criterion, and can compensate the range shift caused by targets' radial velocity. The results of this paper can help the SF waveform design and the processing algorithm development for HRR profiling and velocity compensation.

cs.IT↗

arXiv2013

Cognitive Random Stepped Frequency Radar with Sparse Recovery

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the non-adaptive mechanism.

eess.SP↗

arXiv2013

Shared quantum control via sharing operation on remote single qutrit

Two qubit operation sharing schemes [J. Phys. B 44 (2011) 165508] are generalized to qutrit ones. Operations to be shared are classified into three different classes in terms of different probabilities (i.e, 1/3, 2/3 and 1). For the latter two classes, ten and three restricted sets of operations are found out, respectively. Moreover, the two generalized schemes are amply compared from four aspects, namely, quantum and classical resource consumption, necessaryoperation complexity, success probability and efficiency. It is found that the second scheme is overall more optimal than the first one as far as three restricted sets of operations are concerned.

quant-ph↗

arXiv2010

Extended Range Profiling in Stepped-Frequency Radar with Sparse Recovery

The newly emerging theory of compressed sensing (CS) enables restoring a sparse signal from inadequate number of linear projections. Based on compressed sensing theory, a new algorithm of high-resolution range profiling for stepped-frequency (SF) radar suffering from missing pulses is proposed. The new algorithm recovers target range profile over multiple coarse-range-bins, providing a wide range profiling capability. MATLAB simulation results are presented to verify the proposed method. Furthermore, we use collected data from real SF radar to generate extended target high-resolution range (HRR) profile. Results are compared with `stretch' based least square method to prove its applicability.

cs.IT↗